scRNAseq Interstitial cells : What happens over time?
Marion Hardy
2024-03-07
Introduction
Data from our collaborator Panagiotis rds file for a SingleCellExperiment object containing the single cell data for the interstitial cells of Hydra Vulgaris during multiples stages of regeneration after bisection:
BUT they mapped it to Hydra Magnipapillata (102 version of 105) “Quantification of the generated single cell libraries was performed using the Salmon-Alevin software suite (Salmon version 1.6.0) against the ncbi Hydra 102 transcriptome.”
The coldata of the object contain cell annotation including
quality metrics: nFeature nCount (not MT percentage interestingly)
batch info: either 2869 (3162 barcodes), 3113 (10352 barcodes), 3271 (13279 barcodes), 3357 (3875 barcodes)
originating experiments (head or foot regeneration)
experimental time points
pseudo-axis assignment (vals.axis ranging from 0-1, increasing in the foot-tentacle direction)
mitotic and apoptotic signatures indices from 0 to 1
The rowdata contains gene annotation, using Entrez-gene identifiers. I have also noticed that in the sce objects there’s
PCA, tSNE and UMAP coordinates for reduced dimensions + corrected for batch values
assay metafeatures hold gene_id, product, gene, is.rib.prot.gene (T/F), HypoMarkers (T/F), ccyle (T/F), apopt (T/F) etc
I converted the sce objects 6.6gb into a seurat object 2.2 gb and checked that all cited parameters could be found in it.
Summary of 3/06 meeting with Hannah and Celina
- check myb as a marker for bipotential progenitors
- relabel some of the clusters (ec3, Gc, endodermal neurons)
- plot cell cycle scores
- plot pseudo-axis
Data loading and upate on the project
This report is a repeat of report 5 but addresses the above-mentioned comments from the meeting with Celina and Hannah.
In this report we’re gonna plot transcription factors of interest over the different timepoints. I also wanted to add cell population counts over time. Maybe I could learn to plot gene expression over time too? Anyway, we’re exploring gene expression programs and cell populations over time.
Changing UMAP resolution
target <- c("NSP4","midasin","mini-COL8","SP-D-like","FH20-X3","CAII",
"CELA3B","zinc-carboxypeptidase","ANO39","TYN1","H2A.2.2","nas2-X2",
"Lwamide-X1","DMRT1","TUBA4A-X1","HTRA3","polRF-X1","ec3A","ec3B",
"nop58-X1","OTP","MEP1A","rhammosyl-O-methyltransferase","hywnt3",
"PPOD1","ks1","hyAlx","ELAV2","POU4","MUC2", "ec3", "grm1","myc",
"myc1","wnt3","ec2","ec1B","ec4","ec1A","en1","en1_NDF1_DANRE",
"ec5","en1","en2A","en2B","myb") I founda myb by ctrl+f in the annotation file. However, in Stefan’s supplementary analysis, myb is described as “g27424” after blasting, I only got a 32% hit (80% cover) in the 102 annotation file…
Uncharacterized protein LOC105848384 [Hydra vulgaris] Hydra vulgaris 243 243 82% 2e-68 32.09% 1023 XP_047129678.1
Subsetting by head/foot and timepoint
Pseudo axis values
Cell cycle values
Apoptosis score
Cell population evolution over time
Regenerating a head
“Raw” cell numbers
| REG_HEAD_t0 | REG_HEAD_t06 | REG_HEAD_t12 | REG_HEAD_t24 | REG_HEAD_t48 | REG_HEAD_t96 | |
|---|---|---|---|---|---|---|
| earlyGc | 684 | 574 | 129 | 208 | 278 | 253 |
| earlyNem | 202 | 209 | 148 | 84 | 128 | 78 |
| earlyNeur | 59 | 65 | 125 | 96 | 102 | 78 |
| ec1A | 67 | 55 | 48 | 64 | 50 | 112 |
| ec1A/ec1B | 0 | 0 | 0 | 0 | 3 | 58 |
| ec2 | 0 | 0 | 0 | 0 | 1 | 56 |
| ec3 | 76 | 64 | 71 | 80 | 65 | 168 |
| ec4 | 0 | 0 | 0 | 2 | 6 | 71 |
| en | 36 | 27 | 29 | 18 | 24 | 56 |
| Gc | 64 | 73 | 66 | 60 | 95 | 115 |
| GranG/ZymoG | 256 | 246 | 204 | 212 | 223 | 320 |
| ISC | 488 | 388 | 420 | 201 | 339 | 384 |
| Nb | 1549 | 1398 | 870 | 553 | 737 | 1288 |
| Sum | 3481 | 3099 | 2110 | 1578 | 2051 | 3037 |
Relative number of cells
| REG_HEAD_t0 | REG_HEAD_t06 | REG_HEAD_t12 | REG_HEAD_t24 | REG_HEAD_t48 | REG_HEAD_t96 | |
|---|---|---|---|---|---|---|
| earlyGc | 19.65 | 18.52 | 6.11 | 13.18 | 13.55 | 8.33 |
| earlyNem | 5.80 | 6.74 | 7.01 | 5.32 | 6.24 | 2.57 |
| earlyNeur | 1.69 | 2.10 | 5.92 | 6.08 | 4.97 | 2.57 |
| ec1A | 1.92 | 1.77 | 2.27 | 4.06 | 2.44 | 3.69 |
| ec1A/ec1B | 0.00 | 0.00 | 0.00 | 0.00 | 0.15 | 1.91 |
| ec2 | 0.00 | 0.00 | 0.00 | 0.00 | 0.05 | 1.84 |
| ec3 | 2.18 | 2.07 | 3.36 | 5.07 | 3.17 | 5.53 |
| ec4 | 0.00 | 0.00 | 0.00 | 0.13 | 0.29 | 2.34 |
| en | 1.03 | 0.87 | 1.37 | 1.14 | 1.17 | 1.84 |
| Gc | 1.84 | 2.36 | 3.13 | 3.80 | 4.63 | 3.79 |
| GranG/ZymoG | 7.35 | 7.94 | 9.67 | 13.43 | 10.87 | 10.54 |
| ISC | 14.02 | 12.52 | 19.91 | 12.74 | 16.53 | 12.64 |
| Nb | 44.50 | 45.11 | 41.23 | 35.04 | 35.93 | 42.41 |
| Sum | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 |
Notice how early Gc decrease almost 5x between 12-24 hours while ISC, GranG and early neurons spike up 2 to 3X. I think that population is called upon to create not only gland cells but can help support other progenitors.
Regenerating a foot
“Raw” cell numbers
| REG_FOOT_t0 | REG_FOOT_t06 | REG_FOOT_t12 | REG_FOOT_t24 | REG_FOOT_t48 | REG_FOOT_t96 | |
|---|---|---|---|---|---|---|
| earlyGc | 60 | 41 | 38 | 51 | 204 | 85 |
| earlyNem | 200 | 266 | 211 | 236 | 86 | 205 |
| earlyNeur | 199 | 201 | 176 | 143 | 66 | 100 |
| ec1A | 51 | 57 | 42 | 54 | 36 | 55 |
| ec1A/ec1B | 59 | 77 | 58 | 51 | 64 | 55 |
| ec2 | 80 | 103 | 77 | 63 | 84 | 51 |
| ec3 | 91 | 87 | 94 | 75 | 56 | 68 |
| ec4 | 49 | 48 | 46 | 43 | 43 | 32 |
| en | 38 | 31 | 28 | 22 | 27 | 19 |
| Gc | 149 | 152 | 143 | 131 | 65 | 93 |
| GranG/ZymoG | 332 | 374 | 280 | 269 | 189 | 196 |
| ISC | 581 | 452 | 401 | 390 | 320 | 287 |
| Nb | 948 | 790 | 731 | 797 | 559 | 713 |
| Sum | 2837 | 2679 | 2325 | 2325 | 1799 | 1959 |
Relative number of cells
| REG_FOOT_t0 | REG_FOOT_t06 | REG_FOOT_t12 | REG_FOOT_t24 | REG_FOOT_t48 | REG_FOOT_t96 | |
|---|---|---|---|---|---|---|
| earlyGc | 2.11 | 1.53 | 1.63 | 2.19 | 11.34 | 4.34 |
| earlyNem | 7.05 | 9.93 | 9.08 | 10.15 | 4.78 | 10.46 |
| earlyNeur | 7.01 | 7.50 | 7.57 | 6.15 | 3.67 | 5.10 |
| ec1A | 1.80 | 2.13 | 1.81 | 2.32 | 2.00 | 2.81 |
| ec1A/ec1B | 2.08 | 2.87 | 2.49 | 2.19 | 3.56 | 2.81 |
| ec2 | 2.82 | 3.84 | 3.31 | 2.71 | 4.67 | 2.60 |
| ec3 | 3.21 | 3.25 | 4.04 | 3.23 | 3.11 | 3.47 |
| ec4 | 1.73 | 1.79 | 1.98 | 1.85 | 2.39 | 1.63 |
| en | 1.34 | 1.16 | 1.20 | 0.95 | 1.50 | 0.97 |
| Gc | 5.25 | 5.67 | 6.15 | 5.63 | 3.61 | 4.75 |
| GranG/ZymoG | 11.70 | 13.96 | 12.04 | 11.57 | 10.51 | 10.01 |
| ISC | 20.48 | 16.87 | 17.25 | 16.77 | 17.79 | 14.65 |
| Nb | 33.42 | 29.49 | 31.44 | 34.28 | 31.07 | 36.40 |
| Sum | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 |
Notice how early neurons only go up after a spike in earlGc. Is the opposite happening here?
Transcription factors
We are looking at transcription factors of interest that, based on bulk RNAseq, appeared active only in the injured animal, not the homeostatic one.
Dotplot
Feature plot
Regenerating a head
It looks like the appearing cells might be the one with the transcription factor activity
Regenerating a foot
Session info
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.5.0 scCustomize_1.1.1 Seurat_5.0.1 SeuratObject_5.0.0
## [5] sp_1.5-1 openxlsx_4.2.5.1 forcats_0.5.2 stringr_1.4.1
## [9] dplyr_1.0.10 purrr_0.3.5 readr_2.1.3 tidyr_1.2.1
## [13] tibble_3.1.8 ggplot2_3.4.0 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 spatstat.explore_3.0-5 reticulate_1.26
## [4] tidyselect_1.2.0 htmlwidgets_1.5.4 grid_4.2.2
## [7] Rtsne_0.16 munsell_0.5.0 codetools_0.2-18
## [10] ica_1.0-3 future_1.29.0 miniUI_0.1.1.1
## [13] withr_2.5.0 spatstat.random_3.0-1 colorspace_2.0-3
## [16] progressr_0.11.0 highr_0.9 knitr_1.41
## [19] rstudioapi_0.14 stats4_4.2.2 ROCR_1.0-11
## [22] ggsignif_0.6.4 tensor_1.5 listenv_0.8.0
## [25] labeling_0.4.2 polyclip_1.10-4 farver_2.1.1
## [28] parallelly_1.32.1 vctrs_0.5.0 generics_0.1.3
## [31] xfun_0.34 timechange_0.1.1 R6_2.5.1
## [34] doParallel_1.0.17 clue_0.3-62 ggbeeswarm_0.7.2
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